Reevaluating the role of education on cognitive decline and brain aging in longitudinal cohorts across 33 Western countries

Reevaluating the role of education on cognitive decline and brain aging in longitudinal cohorts across 33 Western countries
  • GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7, e105–e125 (2022).

    Google Scholar 

  • Wolters, F. J. et al. Twenty-seven-year time trends in dementia incidence in Europe and the United States: the Alzheimer Cohorts Consortium. Neurology 95, e519–e531 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen, Y. et al. Dementia incidence trend in England and Wales, 2002–19, and projection for dementia burden to 2040: analysis of data from the English Longitudinal Study of Ageing. Lancet Public Health 8, e859–e867 (2023).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Gerstorf, D. et al. Today’s older adults are cognitively fitter than older adults were 20 years ago, but when and how they decline is no different than in the past. Psychol. Sci. 34, 22–34 (2023).

    PubMed 

    Google Scholar 

  • Livingston, G. et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 404, 572–628 (2024).

    PubMed 

    Google Scholar 

  • Suemoto, C. K. et al. Risk factors for dementia in Brazil: differences by region and race. Alzheimers Dement. 19, 1849–1857 (2023).

    PubMed 

    Google Scholar 

  • Lock, S. L., Chura, L. R., Dilworth-Anderson, P. & Peterson, J. Equity across the life course matters for brain health. Nat. Aging 3, 466–468 (2023).

    PubMed 

    Google Scholar 

  • Ritchie, H., Samborska, V., Ahuja, N., Ortiz-Ospina, E. & Roser, M. Global Education. Our World in Data (2023).

  • Opdebeeck, C., Martyr, A. & Clare, L. Cognitive reserve and cognitive function in healthy older people: a meta-analysis. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 23, 40–60 (2016).

    PubMed 

    Google Scholar 

  • Sepulcre, J. College education as a modulator of the aging brain. Nat. Aging 1, 980–981 (2021).

    PubMed 

    Google Scholar 

  • Cabeza, R. et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci. 19, 701–710 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nyberg, L., Lovden, M., Riklund, K., Lindenberger, U. & Backman, L. Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292–305 (2012).

    PubMed 

    Google Scholar 

  • Arenaza-Urquijo, E. M. et al. Association between educational attainment and amyloid deposition across the spectrum from normal cognition to dementia: neuroimaging evidence for protection and compensation. Neurobiol. Aging 59, 72–79 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Gazzina, S. et al. Education modulates brain maintenance in presymptomatic frontotemporal dementia. J. Neurol. Neurosurg. Psychiatry 90, 1124–1130 (2019).

    PubMed 

    Google Scholar 

  • Del Ser, T., Hachinski, V., Merskey, H. & Munoz, D. G. An autopsy-verified study of the effect of education on degenerative dementia. Brain 122, 2309–2319 (1999).

    PubMed 

    Google Scholar 

  • Nyberg, L. et al. Educational attainment does not influence brain aging. Proc. Natl Acad. Sci. USA 118, e2101644118 (2021).

  • Stern, Y., Barnes, C. A., Grady, C., Jones, R. N. & Raz, N. Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience. Neurobiol. Aging 83, 124–129 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 8, 448–460 (2002).

    PubMed 

    Google Scholar 

  • Stern, Y. et al. A framework for concepts of reserve and resilience in aging. Neurobiol. Aging 124, 100–103 (2023).

    PubMed 

    Google Scholar 

  • Lovden, M. et al. No moderating influence of education on the association between changes in hippocampus volume and memory performance in aging. Aging Brain 4, 100082 (2023).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lovden, M., Fratiglioni, L., Glymour, M. M., Lindenberger, U. & Tucker-Drob, E. M. Education and cognitive functioning across the life span. Psychol. Sci. Public Interest 21, 6–41 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Seblova, D., Berggren, R. & Lovden, M. Education and age-related decline in cognitive performance: systematic review and meta-analysis of longitudinal cohort studies. Ageing Res. Rev. 58, 101005 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • Schneeweis, N., Skirbekk, V. & Winter-Ebmer, R. Does education improve cognitive performance four decades after school completion? Demography 51, 619–643 (2014).

    PubMed 

    Google Scholar 

  • Glymour, M. M., Kawachi, I., Jencks, C. S. & Berkman, L. F. Does childhood schooling affect old age memory or mental status? Using state schooling laws as natural experiments. J. Epidemiol. Community Health 62, 532–537 (2008).

    CAS 
    PubMed 

    Google Scholar 

  • Gorman, E. Does schooling have lasting effects on cognitive function? Evidence from compulsory schooling laws. Demography 60, 1139–1161 (2023).

    PubMed 

    Google Scholar 

  • Brinch, C. N. & Galloway, T. A. Schooling in adolescence raises IQ scores. Proc. Natl Acad. Sci. USA 109, 425–430 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • Lager, A., Seblova, D., Falkstedt, D. & Lovden, M. Cognitive and emotional outcomes after prolonged education: a quasi-experiment on 320 182 Swedish boys. Int. J. Epidemiol. 46, 303–311 (2017).

    PubMed 

    Google Scholar 

  • Courtin, E. et al. Long-term effects of compulsory schooling on physical, mental and cognitive ageing: a natural experiment. J. Epidemiol. Community Health 73, 370–376 (2019).

    PubMed 

    Google Scholar 

  • Ritchie, S. J. & Tucker-Drob, E. M. How much does education improve intelligence? A meta-analysis. Psychol. Sci. 29, 1358–1369 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Walhovd, K. B., Lovden, M. & Fjell, A. M. Timing of lifespan influences on brain and cognition. Trends Cogn. Sci. 27, 901–915 (2023).

    PubMed 

    Google Scholar 

  • Van Hootegem, A., Rogeberg, O., Bratsberg, B. & Lyngstad, T. H. Correlation between cognitive ability and educational attainment weakens over birth cohorts. Sci. Rep. 13, 17747 (2023).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Calandri, I. L. et al. Sex and socioeconomic disparities in dementia risk: a population attributable fractions analysis in Argentina. Neuroepidemiology 58, 264–275 (2024).

    PubMed 

    Google Scholar 

  • Walhovd, K. B. et al. Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cereb. Cortex 32, 839–854 (2022).

    PubMed 

    Google Scholar 

  • Paradela, R. S. et al. Population attributable fractions for risk factors for dementia in seven Latin American countries: an analysis using cross-sectional survey data. Lancet Glob. Health 12, e1600–e1610 (2024).

    CAS 
    PubMed 

    Google Scholar 

  • Gross, A. L. et al. Harmonisation of later-life cognitive function across national contexts: results from the Harmonized Cognitive Assessment Protocols. Lancet Healthy Longev. 4, e573–e583 (2023).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Tulving, E. Episodic memory: from mind to brain. Annu. Rev. Psychol. 53, 1–25 (2002).

    PubMed 

    Google Scholar 

  • Nyberg, L. in The Sage Handbook of Cognitive and Systems Neuroscience Vol. 1 (eds Boyle, G. J. et al.) 47–60 (Sage Publications, 2023).

  • Nyberg, L. et al. Biological and environmental predictors of heterogeneity in neurocognitive ageing: evidence from Betula and other longitudinal studies. Ageing Res. Rev. 64, 101184 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • Borsch-Supan, A. et al. Data resource profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). Int. J. Epidemiol. 42, 992–1001 (2013).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Walhovd, K. B. et al. Healthy minds 0–100 years: optimising the use of European brain imaging cohorts (‘Lifebrain’). Eur. Psychiatry 50, 47–56 (2018).

    PubMed 

    Google Scholar 

  • Mehrbrodt, T., Gruber, S. & Wagner, M. Scales and Multi-item Indicators in the Survey of Health, Ageing and Retirement in Europe (SHARE-ERIC, 2019); https://share-eric.eu/fileadmin/user_upload/SHARE_Working_Paper/WP_Series_45_2019.pdf

  • Zhang, Y. S. et al. Educational attainment and later-life cognitive function in high- and middle-income countries: evidence from the Harmonized Cognitive Assessment Protocol. J. Gerontol. B Psychol. Sci. Soc. Sci. 79, gbae005 (2024).

  • Wood, S. N. & Scheipl, F. gamm4: generalized additive mixed models using ‘mgcv’ and ‘lme4’. R package version 0.2-6 (2020).

  • Cadar, D. et al. An international evaluation of cognitive reserve and memory changes in early old age in 10 European countries. Neuroepidemiology 48, 9–20 (2017).

    PubMed 

    Google Scholar 

  • Walhovd, K. B. et al. Brain aging differs with cognitive ability regardless of education. Sci. Rep. 12, 13886 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Heilbronner, R. L. et al. Official position of the American Academy of Clinical Neuropsychology on serial neuropsychological assessments: the utility and challenges of repeat test administrations in clinical and forensic contexts. Clin. Neuropsychol. 24, 1267–1278 (2010).

    PubMed 

    Google Scholar 

  • Tucker-Drob, E. M., Johnson, K. E. & Jones, R. N. The cognitive reserve hypothesis: a longitudinal examination of age-associated declines in reasoning and processing speed. Dev. Psychol. 45, 431–446 (2009).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Strand, S., Deary, I. J. & Smith, P. Sex differences in cognitive abilities test scores: a UK national picture. Br. J. Educ. Psychol. 76, 463–480 (2006).

    PubMed 

    Google Scholar 

  • Eurostat Regional Yearbook, 2024 Edition (Publications Office of the European Union, 2024); https://ec.europa.eu/eurostat/web/products-flagship-publications/w/ks-ha-24-001

  • Evans, M. D. R., Kelley, J., Sikora, J. & Treiman, D. J. Family scholarly culture and educational success: books and schooling in 27 nations. Res. Soc. Stratif. Mobil. 28, 171–197 (2010).

    Google Scholar 

  • Deary, I. J., Pattie, A. & Starr, J. M. The stability of intelligence from age 11 to age 90 years: the Lothian birth cohort of 1921. Psychol. Sci. 24, 2361–2368 (2013).

    PubMed 

    Google Scholar 

  • Bratsberg, B., Fjell, A. M., Rogeberg, O. J., Skirbekk, V. F. & Walhovd, K. B. Differences in cognitive function at 18 y of age explain the association between low education and early dementia risk. Proc. Natl Acad. Sci. USA 121, e2412017121 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sharp, E. S. & Gatz, M. Relationship between education and dementia: an updated systematic review. Alzheimer Dis. Assoc. Disord. 25, 289–304 (2011).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Fürtjes, A. E. et al. Measurement characteristics and genome-wide correlates of lifetime brain atrophy estimated from a single MRI. Nat. Commun. (in the press).

  • van Loenhoud, A. C., Groot, C., Vogel, J. W., van der Flier, W. M. & Ossenkoppele, R. Is intracranial volume a suitable proxy for brain reserve? Alzheimers Res. Ther. 10, 91 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Engvig, A. et al. Effects of cognitive training on gray matter volumes in memory clinic patients with subjective memory impairment. J. Alzheimers Dis. 41, 779–791 (2014).

    PubMed 

    Google Scholar 

  • Engvig, A. et al. Effects of memory training on cortical thickness in the elderly. Neuroimage 52, 1667–1676 (2010).

    PubMed 

    Google Scholar 

  • Lovden, M. et al. Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiol. Aging 33, 620.e9–620.e22 (2012).

    PubMed 

    Google Scholar 

  • Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367, eaay6690 (2020).

  • Brathen, A. C. S. et al. Cognitive and hippocampal changes weeks and years after memory training. Sci. Rep. 12, 7877 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • de Lange, A. G., Brathen, A. C. S., Rohani, D. A., Fjell, A. M. & Walhovd, K. B. The temporal dynamics of brain plasticity in aging. Cereb. Cortex 28, 1857–1865 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Judd, N. & Kievit, R. No effect of additional education on long-term brain structure—a preregistered natural experiment in thousands of individuals. eLife 13, RP101526 (2025).

    Google Scholar 

  • Anderson, E. L. et al. Education, intelligence and Alzheimer’s disease: evidence from a multivariable two-sample Mendelian randomization study. Int. J. Epidemiol. 49, 1163–1172 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ackerman, P. L. Adult intelligence: the construct and the criterion problem. Perspect. Psychol. Sci. 12, 987–998 (2017).

    PubMed 

    Google Scholar 

  • Li, W. et al. Timing in Early Childhood Education: How Cognitive and Achievement Program Impacts Vary by Starting Age, Program Duration, and Time since the End of the Program (Annenberg Institute for School Reform at Brown University, 2020); https://eric.ed.gov/?id=ED610271

  • Sameroff, A. J., Seifer, R., Baldwin, A. & Baldwin, C. Stability of intelligence from preschool to adolescence: the influence of social and family risk factors. Child Dev. 64, 80–97 (1993).

    CAS 
    PubMed 

    Google Scholar 

  • Tucker-Drob, E. M. & Briley, D. A. Continuity of genetic and environmental influences on cognition across the life span: a meta-analysis of longitudinal twin and adoption studies. Psychol. Bull. 140, 949–979 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Edwin, T. H. et al. Trajectories of occupational cognitive demands and risk of mild cognitive impairment and dementia in later life: the HUNT4 70+ study. Neurology 102, e209353 (2024).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Kivimaki, M. et al. Cognitive stimulation in the workplace, plasma proteins, and risk of dementia: three analyses of population cohort studies. BMJ 374, n1804 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lipnicki, D. M. et al. Age-related cognitive decline and associations with sex, education and apolipoprotein E genotype across ethnocultural groups and geographic regions: a collaborative cohort study. PLoS Med. 14, e1002261 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ritchie, S. J., Bates, T. C. & Deary, I. J. Is education associated with improvements in general cognitive ability, or in specific skills? Dev. Psychol. 51, 573–582 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Fjell, A. M. et al. What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog. Neurobiol. 117, 20–40 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gruber, S., Hunkler, C. & Stuck, S. Generating easySHARE: Guidelines, Structure, Content and Programming (SHARE-ERIC, 2014); https://share-eric.eu/fileadmin/user_upload/SHARE_Working_Paper/SHARE_WP_Series_17_2014.pdf

  • Bergmann, M., Kneip, T., De Luca, G. & Scherpenzeel, A. Survey Participation in the Survey of Health, Ageing and Retirement in Europe (SHARE), Wave 1-7: Based on Release 7.0.0 (SHARE-ERIC, 2019); https://share-eric.eu/fileadmin/user_upload/SHARE_Working_Paper/WP_Series_41_2019_Bergmann_et_al.pdf

  • Börsch-Supan, A. & Gruber, S. easySHARE. Release version: 8.0.0. SHARE-ERIC (2022).

  • Walhovd, K. B. et al. Neurodevelopmental origins of lifespan changes in brain and cognition. Proc. Natl Acad. Sci. USA 113, 9357–9362 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nilsson, L. G. et al. The Betula prospective cohort study: memory, health, and aging. Aging Neuropsychol. Cogn. 4, 1–32 (1997).

    Google Scholar 

  • Nilsson, L.-G. et al. Betula: a prospective cohort study on memory, health and aging. Aging Neuropsychol. Cogn. 11, 134–148 (2004).

    Google Scholar 

  • Rajaram, S. et al. The Walnuts and Healthy Aging Study (WAHA): protocol for a nutritional intervention trial with walnuts on brain aging. Front. Aging Neurosci. 8, 333 (2016).

    PubMed 

    Google Scholar 

  • Vidal-Piñeiro, D. et al. Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging. Brain Stimul. 7, 287–296 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Bertram, L. et al. Cohort profile: the Berlin Aging Study II (BASE-II). Int. J. Epidemiol. 43, 703–712 (2014).

    PubMed 

    Google Scholar 

  • Gerstorf, D. et al. Editorial. Gerontology 62, 311–315 (2016).

    PubMed 

    Google Scholar 

  • Shafto, M. A. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol. 14, 204 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Idland, A. V. et al. Biomarker profiling beyond amyloid and tau: cerebrospinal fluid markers, hippocampal atrophy, and memory change in cognitively unimpaired older adults. Neurobiol. Aging 93, 1–15 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • Mueller, S. G. et al. The Alzheimer’s Disease Neuroimaging Initiative. Neuroimaging Clin. N. Am. 15, 869–877 (2005).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Cattaneo, G. et al. The Barcelona Brain Health Initiative: a cohort study to define and promote determinants of brain health. Front. Aging Neurosci. 10, 321 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Dagley, A. et al. Harvard Aging Brain Study: dataset and accessibility. Neuroimage 144, 255–258 (2017).

    PubMed 

    Google Scholar 

  • Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Breitner, J. C. S., Poirier, J., Etienne, P. E. & Leoutsakos, J. M. Rationale and structure for a new Center for Studies on Prevention of Alzheimer’s Disease (StoP-AD). J. Prev. Alzheimers Dis. 3, 236–242 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • Tremblay-Mercier, J. et al. Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer’s disease. Neuroimage Clin. 31, 102733 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • LaMontagne, P. J. et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. Preprint at medRxiv (2019).

  • Kremen, W. S., Franz, C. E. & Lyons, M. J. Current status of the Vietnam Era Twin Study of Aging (VETSA). Twin Res. Hum. Genet. 22, 783–787 (2019).

    PubMed 

    Google Scholar 

  • Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418 (2012).

    PubMed 

    Google Scholar 

  • Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).

    CAS 
    PubMed 

    Google Scholar 

  • Fischl, B., Sereno, M. I., Tootell, R. B. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    CAS 
    PubMed 

    Google Scholar 

  • Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010).

    PubMed 

    Google Scholar 

  • Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    CAS 
    PubMed 

    Google Scholar 

  • Rutherford, S. et al. Charting brain growth and aging at high spatial precision. eLife 11, e72904 (2022).

  • Rutherford, S. et al. The normative modeling framework for computational psychiatry. Nat. Protoc. 17, 1711–1734 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).

  • Kia, S. M. et al. Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression. PLoS ONE 17, e0278776 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Google Scholar 

  • Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Google Scholar 

  • Vidal-Piñeiro, D. et al. Reliability of structural brain change in cognitively healthy adult samples. Imaging Neurosci. 3, imag_a_00547 (2025).

  • R: a language and environment for statistical computing. R Foundation for Statistical Computing (2024).

  • Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

    Google Scholar 

  • Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Hofman, M. D. & Gelman, A. The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 1593–1623 (2014).

    Google Scholar 

  • Wickham, H. ggplot2: Elegant Graphics for Data Analysis 2nd edn (Springer, 2016).

  • Mowinckel, A. M. & Vidal-Piñeiro, D. Visualization of brain statistics with R packages ggseg and ggseg3d. Adv. Methods Pract. Psychol. Sci. 3, 466–483 (2020).

    Google Scholar 

  • Robinson, D. fuzzyjoin: join tables together on inexact matching. (2020).

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